What is overfitting and how can you prevent it?

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Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers. As a result, the model performs very well on training data but poorly on unseen or test data, because it fails to generalize.

Signs of Overfitting

• High accuracy on training data

• Low accuracy on validation/test data

• Large gap between training and test performance

Causes of Overfitting

• A model that's too complex (e.g., too many layers, parameters)

• Too little training data

• Too many training epochs

• Noisy or irrelevant features

How to Prevent Overfitting

1. Simplify the Model
   Use fewer parameters or simpler algorithms to reduce complexity.

2. Cross-Validation
   Use techniques like k-fold cross-validation to ensure the model generalizes well across different subsets of data.

3. Regularization
   Add penalties to the loss function to discourage complexity.

   • L1 (Lasso) and L2 (Ridge) regularization are common methods.

4. Early Stopping
   Stop training when the validation loss stops improving, instead of continuing until training loss is minimized.

5. Pruning (for decision trees or neural nets)
   Reduce model size by trimming branches or unnecessary parameters.

6. Dropout (for neural networks)
   Randomly deactivate some neurons during training to prevent dependency on specific features.

7. Increase Training Data
   Collect more data or use data augmentation to make the model learn more general patterns.

In summary, overfitting reduces a model’s ability to generalize. It can be controlled using a mix of regularization, validation, simpler models, and better data practices.

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